Scale Invariants from Gaussian-Geometric Moments

Scale invariants of Tchebichef moments are usually achieved by a linear combination of corresponding invariants of geometric moments or via an iterative algorithm to eliminate the scale factor. According to the properties of Tchebichef polynomials, we propose a new approach to construct scale invariants of Tchebichef moments. An algorithm based on matrix multiplication is also provided to efficiently compute the 3D moments and invariants. Several experiments are carried out to validate the effectiveness of our descriptors and algorithm.

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Accurate 2D and 3D images classification using translation and scale invariants of Meixner moments

Multimedia Tools and Applications ◽

10.1007/s11042-020-10311-y ◽

2021 ◽

Author(s):

M. Yamni ◽

A. Daoui ◽

O. El ogri ◽

H. Karmouni ◽

M. Sayyouri ◽

...

Keyword(s):

3D Images ◽

Meixner Moments ◽

2D And 3D ◽

Scale Invariants

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FAST COMPUTATION OF GEOMETRIC MOMENTS AND INVARIANTS USING SCHLICK'S APPROXIMATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001408006764 ◽

2008 ◽

Vol 22 (07) ◽

pp. 1363-1377 ◽

Cited By ~ 3

Author(s):

R. MUKUNDAN

Keyword(s):

Fast Computation ◽

Recent Past ◽

Computational Overhead ◽

Geometric Moments ◽

Alternative Approach ◽

Computational Speed ◽

Moment Functions ◽

The Moment ◽

Moment Integral ◽

Modified Moments

Geometric moments have been used in several applications in the field of Computer Vision. Many techniques for fast computation of geometric moments have therefore been proposed in the recent past, but these algorithms mainly rely on properties of the moment integral such as piecewise differentiability and separability. This paper explores an alternative approach to approximating the moment kernel itself in order to get a notable improvement in computational speed. Using Schlick's approximation for the normalized kernel of geometric moments, the computational overhead could be significantly reduced and numerical stability increased. The paper also analyses the properties of the modified moment functions, and shows that the proposed method could be effectively used in all applications where normalized Cartesian moment kernels are used. Several experimental results showing the invariant characteristics of the modified moments are also presented.

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Hand gesture through geometric moments (HCI based)

2018 2nd International Conference on Inventive Systems and Control (ICISC) ◽

10.1109/icisc.2018.8398862 ◽

2018 ◽

Cited By ~ 2

Author(s):

Santosh Kumar ◽

Arvind Kaurav

Keyword(s):

Hand Gesture ◽

Geometric Moments

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2D Legendre Moments-Based Visual Control

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.162.487 ◽

2012 ◽

Vol 162 ◽

pp. 487-496 ◽

Cited By ~ 1

Author(s):

Aurelien Yeremou Tamtsia ◽

Youcef Mezouar ◽

Philippe Martinet ◽

Haman Djalo ◽

Emmanuel Tonye

Keyword(s):

Visual Servoing ◽

Dynamic Range ◽

High Sensitivity ◽

High Dynamic Range ◽

Interaction Matrix ◽

Geometric Moments ◽

Control Scheme ◽

Control Schemes ◽

Legendre Moments ◽

Set Of Points

Among region-based descriptors, geometric moments have been widely exploited to design visual servoing schemes. However, they present several disadvantages such as high sensitivity to noise measurement, high dynamic range and information redundancy (since they are not computed onto orthogonal basis). In this paper, we propose to use a class of orthogonal moments (namely Legendre moments) instead of geometric moments to improve the behavior of moment-based control schemes. The descriptive form of the interaction matrix related to the Legendre moments computed from a set of points is rst derived. Six visual features are then selected to design a partially-decoupled control scheme. Finally simulated and experimental results are presented to illustrate the validity of our proposal.

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Convolutional Neural Networks and Geometric Moments to Identify the Bilateral Symmetric Midplane in Facial Skeletons from CT Scans

Biology ◽

10.3390/biology10030182 ◽

2021 ◽

Vol 10 (3) ◽

pp. 182

Author(s):

Rodrigo Dalvit Carvalho da Silva ◽

Thomas Richard Jenkyn ◽

Victor Alexander Carranza

Keyword(s):

Neural Network ◽

Neural Networks ◽

Surgical Planning ◽

Good Accuracy ◽

Bilateral Symmetry ◽

Ct Scans ◽

Facial Skeleton ◽

Intact Side ◽

Geometric Moments ◽

The Face

In reconstructive craniofacial surgery, the bilateral symmetry of the midplane of the facial skeleton plays an important role in surgical planning. Surgeons can take advantage of the intact side of the face as a template for the malformed side by accurately locating the midplane to assist in the preparation of the surgical procedure. However, despite its importance, the location of the midline is still a subjective procedure. The aim of this study was to present a 3D technique using a convolutional neural network and geometric moments to automatically calculate the craniofacial midline symmetry of the facial skeleton from CT scans. To perform this task, a total of 195 skull images were assessed to validate the proposed technique. In the symmetry planes, the technique was found to be reliable and provided good accuracy. However, further investigations to improve the results of asymmetric images may be carried out.

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Algorithms for 3D Particles Characterization Using X-Ray Microtomography in Proppant Crush Test

Journal of Imaging ◽

10.3390/jimaging4110134 ◽

2018 ◽

Vol 4 (11) ◽

pp. 134 ◽

Cited By ~ 6

Author(s):

Ilia Safonov ◽

Ivan Yakimchuk ◽

Vladimir Abashkin

Keyword(s):

Laboratory Method ◽

X Ray ◽

Geometric Moments ◽

Before And After ◽

Statistical Hypotheses ◽

Image Processing Algorithms ◽

A New Technique ◽

Individual Particles ◽

Remarkable Advantage

We present image processing algorithms for a new technique of ceramic proppant crush resistance characterization. To obtain the images of the proppant material before and after the test we used X-ray microtomography. We propose a watershed-based unsupervised algorithm for segmentation of proppant particles, as well as a set of parameters for the characterization of 3D particle size, shape, and porosity. An effective approach based on central geometric moments is described. The approach is used for calculation of particles’ form factor, compactness, equivalent ellipsoid axes lengths, and lengths of projections to these axes. Obtained grain size distribution and crush resistance fit the results of conventional test measured by sieves. However, our technique has a remarkable advantage over traditional laboratory method since it allows to trace the destruction at the level of individual particles and their fragments; it grants to analyze morphological features of fines. We also provide an example describing how the approach can be used for verification of statistical hypotheses about the correlation between particles’ parameters and their crushing under load.

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